Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F30/00—Computer-aided design [CAD]
  • G06F30/20—Design optimisation, verification or simulation
  • G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N10/00—Quantum computing, i.e. information processing based on quantum-mechanical phenomena
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
  • G06T17/20—Finite element generation, e.g. wire-frame surface description, tesselation
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B82—NANOTECHNOLOGY
  • B82Y—SPECIFIC USES OR APPLICATIONS OF NANOSTRUCTURES; MEASUREMENT OR ANALYSIS OF NANOSTRUCTURES; MANUFACTURE OR TREATMENT OF NANOSTRUCTURES
  • B82Y10/00—Nanotechnology for information processing, storage or transmission, e.g. quantum computing or single electron logic

Definitions

• a computing device including memory storing a quantum computing device model.
• the quantum computing device model may include a plurality of quantum computing device components having a respective plurality of actual boundaries.
• the computing device may further include a processor configured to generate a first discretized model of the quantum computing device model.
• the first discretized model may divide the quantum computing device model into a first plurality of cells and may indicate a respective estimated boundary for each quantum computing device component.
• the processor may be further configured to solve a first differential equation discretized with the first discretized model.
• the processor may be further configured to generate a second discretized model of a focus region of the quantum computing device model.
• the second discretized model may divide the focus region into a second plurality of cells. In the second discretized model, the focus region may have the estimated boundary computed for the focus region in the first discretized model.
• the processor may be further configured to solve a second differential equation discretized with the second discretized model.
• FIG. 1 schematically shows an example computing device including memory storing a quantum computing device model, according to one embodiment of the present disclosure.
• FIG. 2 shows an example quantum computing device model, according to the embodiment of FIG. 1.
• FIG. 3 shows an example first discretized model of the quantum computing device model of FIG. 2, wherein the first discretized model is a finite difference model.
• FIG. 4 shows another example first discretized model that is a finite element model, according to the embodiment of FIG. 1.
• FIG. 5 shows an example second discretized model generated from the quantum computing device model of FIG. 2 and the first discretized model of FIG. 3.
• FIG. 6 shows an example graphical user interface of the computing device of FIG. 1.
• FIG. 7 shows a flowchart of an example method that may be used with a computing device, according to the embodiment of FIG. 1.
• FIG. 8 shows additional steps that may be performed when performing the method of FIG. 7.
• FIG. 9 shows a schematic view of an example computing environment in which the computer device of FIG. 1 may be enacted.
• the simulation typically models different portions of the quantum computing device at different resolution levels. For example, a model of a superconductor included in the quantum computing device may be modeled at a higher resolution (corresponding to a shorter length scale) than a semiconductor included in the same quantum computing device. Each region that is modeled may be discretized, and a respective differential equation may be solved numerically over the region using the discretization.
• the simulation of the quantum computing device may include boundaries between regions of the quantum computing device that are discretized with different resolutions.
• areas of the model near a boundary may be erroneously treated as included in more than one component of the quantum computing device due to differences in the location of the boundary in different discretizations.
• a point near the boundary between a semiconductor and a superconductor may be modeled as though it were both a semiconductor and a superconductor, leading to unphysical results that do not match experimentally observed behavior of the quantum computing device.
• a point near a boundary may be modeled as though it were not included in any component of the quantum computing device.
• differences in resolution may lead to unphysical results when multiple differential equations are solved over a region of the quantum computing device and the results of one differential equation are used as inputs when solving another differential equation. For example, if an electrostatic potential is computed for a quantum computing device component that is discretized at a low resolution, unphysical artifacts may occur if the computed electrostatic potential is used as an input when solving the Bogoliubov-de Gennes equation within a superconductor at a higher resolution.
• the computing device 10 may include a processor 12 and memory 14, which may be operatively coupled.
• the processor 12 and the memory 14 may each be instantiated in one or more physical components such as one or more processor cores and/or one or more physical memory modules.
• the functions of the computing device 10 may be distributed across a plurality of communicatively coupled computing devices.
• the computing device 10 may further include one or more user input devices 16.
• the computing device 10 may further include one or more output devices, which may include a display 18.
• the display 18 may be configured to display a graphical user interface (GUI) 70 via which a user may view output communicated to the display 18 by the processor 12.
• the GUI 70 may be further configured to receive user input via the one or more user input devices 16.
• GUI graphical user interface
• the memory 14 of the computing device 10 may store a quantum computing device model 20.
• the quantum computing device model 20 may include a plurality of quantum computing device components 22 having a respective plurality of actual boundaries 24.
• the quantum computing device model 20 may be a computer- aided design (CAD) model of a quantum computing device.
• the quantum computing device model 20 may be a two-dimensional model of a cross-section of a quantum computing device.
• the quantum computing device model may be a three-dimensional model.
• the plurality of quantum computing device components 22 may be indicated in the quantum computing device model 20 as non-overlapping regions of a two-dimensional space or a three-dimensional volume.
• FIG. 2 An example quantum computing device model 20 is depicted in FIG. 2.
• the quantum computing device model 20 is a two-dimensional model of a cross-section of a two-facet nanowire.
• the quantum computing device components 22 shown in the cross-section are a semiconductor 22A, a superconductor 22B, and a gate dielectric 22C.
• One of the actual boundaries 24 shown in the example of FIG. 2 is a boundary between the semiconductor 22A and the superconductor 22B.
• discretization artifacts may occur at boundaries between semiconductors and superconductors when existing methods of modeling quantum computing devices are used.
• the quantum computing device model 20 may, in some embodiments, further indicate a respective material 26 of each quantum computing device component 22.
• the material 26A of the semiconductor 22A is indium arsenide
• the material 26B of the superconductor 22B is aluminum
• the material 26C of the gate dielectric 22C is hafnium dioxide.
• the memory 14 may further store a respective part identifier 28 associated with each quantum computing device component 22.
• the memory 14 may indicate the respective materials 26 of the quantum computing device components 22 by storing a table associating each part identifier 28 with a material 26.
• the processor 12 may be configured to generate a first discretized model 30 of the quantum computing device model 20.
• the first discretized model 30 may divide the quantum computing device model 20 into a first plurality of cells 32.
• the cells 34 included in the first plurality of cells 32 may be non-overlapping regions of the quantum computing device model 20, such that no points in the quantum computing device model 20 are included in two or more cells 34 at a time.
• each cell 34 may be a rectangular cell or may have some other shape.
• each cell 34 may be a rectangular prism or may have some other three-dimensional form.
• the first discretized model 30 may indicate a respective estimated boundary 36 for each quantum computing device component 22.
• the estimated boundary 36 of a quantum computing device component 22 may differ from the actual boundary 24 indicated for that quantum computing device component 22 in the quantum computing device model 20.
• the first discretized model 30 may indicate the estimated boundaries 36 of the quantum computing device components 22 with a table that maps points in space to part identifiers 28 of quantum computing device components 22.
• FIG. 3 shows an example first discretized model 30 generated from the quantum computing device model 20 of FIG. 2. In the example of FIG. 3, the quantum computing device model 20 is divided into a plurality of square cells 34.
• the respective estimated boundaries 36A and 36B of the semiconductor 22A and the superconductor 22B do not match their respective actual boundaries 24.
• the estimated boundary 36C of the gate dielectric 22C matches the actual boundary 24 of the gate dielectric 22C.
• the processor 12 may be further configured to solve a first differential equation 40 discretized with the first discretized model 30.
• the first differential equation 40 may be selected from the group consisting of a Schrodinger equation, a Poisson equation, and a Bogoliubov-de Gennes equation.
• the first differential equation 40 may be a nonlinear Poisson equation with a Thomas-Fermi density functional.
• the processor 12 may use the first discretized model 30 to specify a plurality of discrete spatial steps at which a numerical solution to the first differential equation 40 is estimated.
• the first differential equation 40 may have one or more boundary conditions 42 specified by the first discretized model 30.
• a boundary condition 42 may specify that an electric field inside an electrical conductor is zero.
• the first discretized model 30 may be a finite difference model.
• the finite difference model may divide the quantum computing device model 20 into a grid of rectangular cells 34, as shown in the example of FIG. 3. Points at the respective centers of the rectangular cells 34 may, for example, be the discrete spatial points at which the processor 12 estimates the numerical solution to the first differential equation 40.
• the first discretized model 30 may be a finite element model.
• the first discretized model 30 may include one or more cells 34 that are non-rectangular in shape.
• the finite element model may include a three-dimensional mesh that includes a plurality of tetrahedra or hexahedra.
• FIG. 4 shows an example first discretized model 130 of a three-dimensional quantum computing device model 20 of a portion of a hexagonal nanowire with an aluminum shell covering two wire facets. In the example of FIG.
• the first discretized model 130 is a mesh that discretizes three-dimensional forms indicating a semiconductor 122A, a superconductor 122B, and a backgate 122C.
• the estimated boundary 36 may coincide with the actual boundary 24 of one or more of the quantum computing device components 22. In other embodiments, such as when curved components are used, the estimated boundary 36 may differ from the actual boundary 24.
• the finite element model may include a bounding box tree 38 indicating, for each of the quantum computing device components 22, one or more cells 34 of the finite element model corresponding to that quantum computing device component 22.
• the bounding box tree 38 may associate each cell 34 with a part identifier 28 of a quantum computing device component 22 in which that cell 34 is included.
• the bounding box tree 38 may encode inclusion relations between the cells 34 of the finite element model and the plurality of quantum computing device components 22.
• each bounding box included in the bounding box tree 38 may be rectangular.
• the bounding box tree 38 in such embodiments, may be an axis aligned bounding box tree.
• the plurality of bounding boxes of the quantum computing device components 22 and cells 34 may be aligned along different axes.
• the processor 12 may be further configured to generate a second discretized model 50 of a focus region 54 of the quantum computing device model 20.
• the focus region 54 may be a region of the quantum computing device model 20 for which a different length scale from that of the first discretized model 30 is used in the simulation of the quantum computing device.
• the focus region 54 may encompass a specific quantum computing device component 22 or a plurality of such quantum computing device components 22.
• the second discretized model 50 may divide the focus region 54 into a second plurality of cells 52.
• One or more of the second plurality of cells 52 may be smaller than one or more of the first plurality of cells 32.
• each cell 34 included in the second plurality of cells 52 may be smaller than each cell 34 included in the first plurality of cells 32.
• the second discretized model 50 includes an estimated focus region boundary 56 that delimits the focus region 54.
• the focus region 54 of the second discretized model 50 may be bounded at least in part by the estimated boundary 36 indicated in the first discretized model 30 for a quantum computing device component 22 of the plurality of quantum computing device components 22.
• the estimated focus region boundary 56 may include one or more portions not specified by the estimated boundary 36, such as an internal boundary within a quantum computing device component 22
• the second discretized model 50 may further indicate one or more quantum computing device components 22 included in the focus region 54.
• the focus region 54 may indicate the respective estimated boundaries 36 of one or more quantum computing device components 22 with a table that maps points in space to part identifiers 28 of quantum computing device components 22.
• the processor 12 is configured to avoid treating points in the quantum computing device model 20 as though they were included in more than one material or no material. Thus, unphysical artifacts that lead the simulation to produce inaccurate results may be avoided when changing the resolution of the simulation in the focus region 54.
• the processor 12 may configured to solve a respective plurality of differential equations for a plurality of quantum computing device components 22. Boundaries between the quantum computing device components 22 included in the quantum computing device model 20 may be set such that the respective estimated focus region boundaries 56 of those quantum computing device components are consistent when each of the plurality of differential equations. Thus, artifacts may be prevented from occurring at internal boundaries between quantum computing device components 22 of the quantum computing device model 20.
• FIG. 5 shows an example of a second discretized model 50 generated based on the first discretized model of FIG. 3.
• the focus region 54 is the region of the first discretized model 30 that corresponds to the superconductor 22B.
• the boundary of the focus region 54 is the estimated boundary 36 that was computed for the superconductor 22B when the first discretized model 30 was computed.
• each cell 34 included in the second plurality of cells 52 of the second discretized model 50 is smaller than each cell 34 included in the first plurality of cells 32 of the first discretized model 30.
• the estimated boundary 36 of the superconductor 22B from the first discretized model 30 is used instead.
• the estimated focus region boundary 56 is the estimated boundary 36B of the superconductor 22B.
• the first discretized model 30 and the second discretized model 50 may differ in dimensionality such that one of the first discretized model 30 and the second discretized model 50 is two-dimensional and the other is three- dimensional.
• the processor 12 may be configured to switch from a finite element model to a finite difference model or from a finite difference model to a finite element model when the second discretized model 50 is generated.
• the processor 12 may be configured to use a finite element model as the first discretized model 30 in three dimensions.
• the processor 12 may be further configured to extract a two-dimensional cross section of the first discretized model 30 and generate the second discretized model 50 from the cross section.
• the second discretized model 50 may be a finite difference model.
• the processor 12 may be further configured to solve a second differential equation 60 discretized with the second discretized model 50.
• the second differential equation 60 may be selected from the group consisting of a Schrodinger equation, a Poisson equation, and a Bogoliubov-de Gennes equation.
• the second differential equation 60 may have one or more boundary conditions 62 that are set based on a solution to the first differential equation 40.
• the one or more boundary conditions 62 may be set to maintain spatial continuity and normalizability of a wavefunction.
• one or more other parameters of the second differential equation 60 may be set based on the solution to the first differential equation 40.
• FIG. 6 shows an example of a GUI 70 which the processor 12 may be further configured to output for display on the display 18.
• the processor 12 may be further configured to receive user input at the GUI 70.
• the user input may specify one or more parameters of the quantum computing device model, the first discretized model, the second discretized model, the first differential equation, and/or the second differential equation.
• the example of FIG. 6 shows the quantum computing device model 20 of FIG. 2 along with a selectable GUI element labeled “Edit device model.”
• the processor 12 may be further configured to output, for display in the GUI 70, one or more additional GUI elements via which the user may modify properties of the quantum computing device model 20.
• FIG. 6 shows GUI elements via which the user may set the parameters of a discretized model and specify a differential equation to solve over the discretized model.
• the processor 12 may be further configured to output a solution to the differential equation specified by the user for display on the display 18.
• the discretized model and the differential equation specified by the user in the example of FIG. 6 may be the first discretized model 30 and the first differential equation 40 respectively or may alternatively be the second discretized model 50 and the second differential equation 60.
• FIG. 7 shows a flowchart of an example method 200 for use with a computing device, which may be the computing device 10 of FIG. 1 or some other computing device.
• the method 200 may include storing a quantum computing device model in memory.
• the quantum computing device model may include a plurality of quantum computing device components, which may have a respective plurality of actual boundaries.
• the quantum computing device model may be a two-dimensional model or a three-dimensional model.
• the quantum computing device model may further indicate a respective material of each quantum computing device component.
• at least one actual boundary of the plurality of actual boundaries included in the quantum computing device model may be a boundary between a superconductor and a semiconductor.
• the quantum computing device model may further include a part identifier for each quantum computing device component.
• the method 200 may further include generating a first discretized model of the quantum computing device model.
• the first discretized model may divide the quantum computing device model into a first plurality of cells.
• the first discretized model may indicate a respective estimated boundary for each quantum computing device component.
• the first discretized model may be a two-dimensional model or a three-dimensional model.
• the first discretized model may be a finite difference model.
• the finite difference model may divide the quantum computing device model into a grid of rectangular cells.
• the first discretized model may be a finite element model.
• the plurality of cells included in the finite element model may be triangular (when the finite element model is two-dimensional) or tetrahedral (when the finite element model is three- dimensional).
• the finite element model may include a bounding box tree.
• the bounding box tree may indicate, for each of the quantum computing device components, one or more cells of the finite element model corresponding to that quantum computing device component.
• the bounding box tree may, for example, be an axis aligned bounding box tree.
• the method 200 may further include solving a first differential equation discretized with the first discretized model.
• the first differential equation may be selected from the group consisting of a Schrodinger equation, a Poisson equation, and a Bogoliubov-de Gennes equation. Alternatively, other differential equations may be used.
• the method 200 may further include generating a second discretized model of a focus region of the quantum computing device model.
• the focus region may be a region of the quantum computing device model for which a different resolution or discretization method from that of the first discretization model is used.
• the focus region may be a quantum computing device component of the plurality of quantum computing device components.
• the focus region may be bounded at least in part by the estimated boundary indicated in the first discretized model for a quantum computing device component of the plurality of quantum computing device components.
• the second discretized model may divide the focus region into a second plurality of cells.
• one or more of the second plurality of cells may be smaller than one or more of the first plurality of cells.
• the second discretized model may be a finite difference model or a finite element model.
• the finite difference model may divide the focus region into a grid of rectangular cells.
• the finite element model may include a bounding box tree, as in the first discretized model.
• FIG. 8 shows additional steps of the method 200 of FIG. 7 that may be performed in embodiments in which the computing device at which the method 200 is performed further includes a display and a user input device.
• the method may further include, at step 212, outputting a GUI for display on the display.
• the method 200 may further include, at step 214, receiving user input at the GUI via the user input device.
• the user input may specify one or more parameters of the quantum computing device model, the first discretized model, the second discretized model, the first differential equation, and/or the second differential equation.
• the method 200 may further include, at step 216, outputting a solution to the first differential equation and/or a solution to the second differential equation for display on the display.
• a third discretized model at a higher resolution than the second discretized model may be generated for at least a portion of the focus region.
• the second discretized model may be treated as the first discretized model, and the above methods for generating the second discretized model may be used to generate the third discretized model.
• the processor may iteratively generate nested discretized models with different length scales.
• a plurality of second discretized models may be generated using the estimated boundaries computed for the quantum computing device components in the first discretized model. These second discretized models may have the same resolution or different resolutions. [0044] Using the devices and methods discussed above, errors that result from setting inconsistent boundaries for quantum computing device components may be avoided when performing computer simulations of quantum computing devices. By using the boundaries defined for quantum computing device components in the first discretization when selecting the focus region of the second discretization, a user performing a simulation of a quantum computing device may avoid the occurrence of unphysical artifacts that may otherwise occur at the boundaries between quantum computing device components. Although the above example embodiments are described with reference to simulating a quantum computing device, the systems and methods described above may also be applied to simulating the physical properties of other types of devices.
• the methods and processes described herein may be tied to a computing system of one or more computing devices.
• such methods and processes may be implemented as a computer-application program or service, an application-programming interface (API), a library, and/or other computer-program product.
• API application-programming interface
• FIG. 9 schematically shows a non-limiting embodiment of a computing system 300 that can enact one or more of the methods and processes described above.
• Computing system 300 is shown in simplified form.
• Computing system 300 may embody the computing device 10 described above and illustrated in FIG. 1.
• Computing system 300 may take the form of one or more personal computers, server computers, tablet computers, home-entertainment computers, network computing devices, gaming devices, mobile computing devices, mobile communication devices (e.g., smart phone), and/or other computing devices, and wearable computing devices such as smart wristwatches and head mounted augmented reality devices.
• Computing system 300 includes a logic processor 302 volatile memory 304, and a non-volatile storage device 306.
• Computing system 300 may optionally include a display subsystem 308, input subsystem 310, communication subsystem 312, and/or other components not shown in FIG. 9.
• Logic processor 302 includes one or more physical devices configured to execute instructions.
• the logic processor may be configured to execute instructions that are part of one or more applications, programs, routines, libraries, objects, components, data structures, or other logical constructs. Such instructions may be implemented to perform a task, implement a data type, transform the state of one or more components, achieve a technical effect, or otherwise arrive at a desired result.
• the logic processor may include one or more physical processors (hardware) configured to execute software instructions. Additionally or alternatively, the logic processor may include one or more hardware logic circuits or firmware devices configured to execute hardware-implemented logic or firmware instructions. Processors of the logic processor 302 may be single-core or multi-core, and the instructions executed thereon may be configured for sequential, parallel, and/or distributed processing. Individual components of the logic processor optionally may be distributed among two or more separate devices, which may be remotely located and/or configured for coordinated processing. Aspects of the logic processor may be virtualized and executed by remotely accessible, networked computing devices configured in a cloud-computing configuration. In such a case, these virtualized aspects are run on different physical logic processors of various different machines, it will be understood.
• Non-volatile storage device 306 includes one or more physical devices configured to hold instructions executable by the logic processors to implement the methods and processes described herein. When such methods and processes are implemented, the state of non-volatile storage device 306 may be transformed — e.g., to hold different data.
• Non-volatile storage device 306 may include physical devices that are removable and/or built-in.
• Non-volatile storage device 306 may include optical memory (e g., CD, DVD, HD-DVD, Blu-Ray Disc, etc ), semiconductor memory (e g., ROM, EPROM, EEPROM, FLASH memory, etc.), and/or magnetic memory (e.g., hard-disk drive, floppy-disk drive, tape drive, MRAM, etc.), or other mass storage device technology.
• Non volatile storage device 306 may include nonvolatile, dynamic, static, read/write, read-only, sequential-access, location-addressable, file-addressable, and/or content-addressable devices. It will be appreciated that non-volatile storage device 306 is configured to hold instructions even when power is cut to the non-volatile storage device 306.
• Volatile memory 304 may include physical devices that include random access memory. Volatile memory 304 is typically utilized by logic processor 302 to temporarily store information during processing of software instructions. It will be appreciated that volatile memory 304 typically does not continue to store instructions when power is cut to the volatile memory 304.
• logic processor 302, volatile memory 304, and non-volatile storage device 306 may be integrated together into one or more hardware-logic components.
• Such hardware-logic components may include field-programmable gate arrays (FPGAs), program- and application-specific integrated circuits (PASIC / ASICs), program- and application-specific standard products (PSSP / ASSPs), system-on-a-chip (SOC), and complex programmable logic devices (CPLDs), for example.
• FPGAs field-programmable gate arrays
• PASIC / ASICs program- and application-specific integrated circuits
• PSSP / ASSPs program- and application-specific standard products
• SOC system-on-a-chip
• CPLDs complex programmable logic devices
• module may be used to describe an aspect of computing system 300 typically implemented in software by a processor to perform a particular function using portions of volatile memory, which function involves transformative processing that specially configures the processor to perform the function.
• a module, program, or engine may be instantiated via logic processor 302 executing instructions held by non-volatile storage device 306, using portions of volatile memory 304.
• modules, programs, and/or engines may be instantiated from the same application, service, code block, object, library, routine, API, function, etc.
• the same module, program, and/or engine may be instantiated by different applications, services, code blocks, objects, routines, APIs, functions, etc.
• the terms “module,” “program,” and “engine” may encompass individual or groups of executable files, data files, libraries, drivers, scripts, database records, etc.
• display subsystem 308 may be used to present a visual representation of data held by non-volatile storage device 306.
• the visual representation may take the form of a graphical user interface (GUI).
• GUI graphical user interface
• the state of display subsystem 308 may likewise be transformed to visually represent changes in the underlying data.
• Display subsystem 308 may include one or more display devices utilizing virtually any type of technology. Such display devices may be combined with logic processor 302, volatile memory 304, and/or non-volatile storage device 306 in a shared enclosure, or such display devices may be peripheral display devices.
• input subsystem 310 may comprise or interface with one or more user-input devices such as a keyboard, mouse, touch screen, or game controller.
• the input subsystem may comprise or interface with selected natural user input (NUI) componentry.
• NUI natural user input
• Such componentry may be integrated or peripheral, and the transduction and/or processing of input actions may be handled on- or off-board.
• Example NUI componentry may include a microphone for speech and/or voice recognition; an infrared, color, stereoscopic, and/or depth camera for machine vision and/or gesture recognition; a head tracker, eye tracker, accelerometer, and/or gyroscope for motion detection and/or intent recognition; as well as electric-field sensing componentry for assessing brain activity; and/or any other suitable sensor.
• communication subsystem 312 may be configured to communicatively couple various computing devices described herein with each other, and with other devices.
• Communication subsystem 312 may include wired and/or wireless communication devices compatible with one or more different communication protocols.
• the communication subsystem may be configured for communication via a wireless telephone network, or a wired or wireless local- or wide-area network, such as aHDMI over Wi-Fi connection.
• the communication subsystem may allow computing system 300 to send and/or receive messages to and/or from other devices via a network such as the Internet.
• a computing device including memory storing a quantum computing device model.
• the quantum computing device model may include a plurality of quantum computing device components having a respective plurality of actual boundaries.
• the computing device may further include a processor configured to generate a first discretized model of the quantum computing device model.
• the first discretized model may divide the quantum computing device model into a first plurality of cells.
• the first discretized model may indicate a respective estimated boundary for each quantum computing device component.
• the processor may be further configured to solve a first differential equation discretized with the first discretized model.
• the processor may be further configured to generate a second discretized model of a focus region of the quantum computing device model.
• the second discretized model may divide the focus region into a second plurality of cells.
• the focus region may be bounded at least in part by the estimated boundary indicated in the first discretized model for a quantum computing device component of the plurality of quantum computing device components.
• the processor may be further configured to solve a second differential equation discretized with the second discretized model.
• the quantum computing device model may further indicate a respective material of each quantum computing device component.
• At least one actual boundary of the plurality of actual boundaries included in the quantum computing device model may be a boundary between a superconductor and a semiconductor.
• At least one of the first discretized model and the second discretized model may be a finite difference model.
• the finite difference model may divide the quantum computing device model into a grid of rectangular cells.
• At least one of the first discretized model and the second discretized model may be a finite element model.
• the finite element model may include a bounding box tree indicating, for each of the quantum computing device components, one or more cells of the finite element model corresponding to that quantum computing device component.
• one or more of the second plurality of cells may be smaller than one or more of the first plurality of cells.
• the first differential equation and the second differential equation may each be selected from the group consisting of a Schrodinger equation, a Poisson equation, and a Bogoliubov-de Gennes equation.
• the quantum computing device model may be a two-dimensional model.
• the quantum computing device model may be a three-dimensional model.
• the computing device may further include a display and a user input device.
• the processor may be further configured to output a graphical user interface (GUI) for display on the display.
• GUI graphical user interface
• the processor may be further configured to receive user input at the GUI specifying one or more parameters of the quantum computing device model, the first discretized model, the second discretized model, the first differential equation, and/or the second differential equation.
• the processor may be further configured to output a solution to the first differential equation and/or a solution to the second differential equation for display on the display.
• a method for use with a computing device is provided. The method may include storing a quantum computing device model in memory.
• the quantum computing device model may include a plurality of quantum computing device components having a respective plurality of actual boundaries.
• the method may further include generating a first discretized model of the quantum computing device model.
• the first discretized model may divide the quantum computing device model into a first plurality of cells.
• the first discretized model may indicate a respective estimated boundary for each quantum computing device component.
• the method may further include solving a first differential equation discretized with the first discretized model.
• the method may further include generating a second discretized model of a focus region of the quantum computing device model.
• the second discretized model may divide the focus region into a second plurality of cells.
• the focus region may be bounded at least in part by the estimated boundary indicated in the first discretized model for a quantum computing device component of the plurality of quantum computing device components.
• the method may further include solving a second differential equation discretized with the second discretized model.
• the quantum computing device model may further indicate a respective material of each quantum computing device component.
• At least one actual boundary of the plurality of actual boundaries included in the quantum computing device model may be a boundary between a superconductor and a semiconductor.
• At least one of the first discretized model and the second discretized model may be a finite difference model.
• At least one of the first discretized model and the second discretized model may be a finite element model.
• one or more of the second plurality of cells may be smaller than one or more of the first plurality of cells.
• the first differential equation and the second differential equation may each be selected from the group consisting of a Schrodinger equation, a Poisson equation, and a Bogoliubov-de Gennes equation.
• a computing device including memory storing a device model.
• the device model may include a plurality of device components having a respective plurality of actual boundaries.
• the computing device may further include a processor configured to generate a first discretized model of the device model.
• the first discretized model may divide the device model into a first plurality of cells via finite difference analysis or finite element analysis.
• the first discretized model may indicate a respective estimated boundary for each device component.
• the processor may be further configured to solve a first differential equation discretized with the first discretized model.
• the processor may be further configured to generate a second discretized model of a focus region of the device model.
• the second discretized model may divide the focus region into a second plurality of cells via finite difference analysis or finite element analysis.
• the focus region may be bounded at least in part by the estimated boundary indicated in the first discretized model for a device component of the plurality of device components.
• One or more of the second plurality of cells are smaller than one or more of the first plurality of cells.
• the processor may be further configured to solve a second differential equation discretized with the second discretized model.

Landscapes

• Engineering & Computer Science (AREA)
• Physics & Mathematics (AREA)
• Theoretical Computer Science (AREA)
• General Physics & Mathematics (AREA)
• Evolutionary Computation (AREA)
• General Engineering & Computer Science (AREA)
• Software Systems (AREA)
• Geometry (AREA)
• Condensed Matter Physics & Semiconductors (AREA)
• Computational Mathematics (AREA)
• Artificial Intelligence (AREA)
• Data Mining & Analysis (AREA)
• Mathematical Analysis (AREA)
• Mathematical Optimization (AREA)
• Pure & Applied Mathematics (AREA)
• Computing Systems (AREA)
• Mathematical Physics (AREA)
• Computer Hardware Design (AREA)
• Computer Graphics (AREA)
• Management, Administration, Business Operations System, And Electronic Commerce (AREA)
• Complex Calculations (AREA)

Applications Claiming Priority (2)

Publications (1)

Family

ID=72340428

Family Applications (1)

Country Status (3)

Families Citing this family (1)

Family Cites Families (8)

• 2019
  • 2019-10-02 US US16/591,443 patent/US11551130B2/en active Active
• 2020
  • 2020-08-21 WO PCT/US2020/047286 patent/WO2021066960A1/en unknown
  • 2020-08-21 EP EP20765420.3A patent/EP4038536A1/de active Pending

Also Published As

Similar Documents

Legal Events